An Automated Method for Landmark Identification and Finite-Element Modeling of the Lumbar Spine

Campbell, J. Quinn; Petrella, Anthony J.

doi:10.1109/tbme.2015.2444811

Cited by 25 publications

(21 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The automated algorithm runs in Matlab (MathWorks, Natick, MA) and requires approximately 90 minutes to complete for a typical multi-segmental lumbar specimen (L1-L5). Additional details of the automated methods may be found in (Campbell and Petrella, 2015).…”

Section: Fe Model Detailsmentioning

confidence: 99%

“…Since the bone elements were rigid, their only function in the model was for visualization of the bone surface and confirmation of correct soft tissue attachment. Seven relevant spinal ligaments were represented using sets of nonlinear tension-only connector elements as described in Campbell and Petrella (2015). The nonlinear ligament properties were based on exponential fits from the literature (Ayturk and Puttlitz, 2011;Nolte et al, 1990;Rohlmann et al, 2006).…”

Section: Fe Model Detailsmentioning

confidence: 99%

“…The facet contacts were defined using frictionless softened contact with a linear pressureoverclosure relationship to define interaction of the sets of rigid cartilage surfaces. The initial gap between the cartilage surfaces was defined relative to the subchondral bone as identified on the CT for each specimen (Campbell and Petrella, 2015). The ligament endpoints, superior, and inferior surfaces of the discs, and interior surface of the cartilage elements were all rigidly fixed to the bones where they attached.…”

Section: Fe Model Detailsmentioning

confidence: 99%

“…Modeling a large number of subjects to represent a target population has been impractical, however, due to the complexity of spinal geometry and the time consuming process of model creation. A new method for automatic generation of FE meshes of the lumbar spine addresses this limitation (Campbell and Petrella, 2015), but it has not been formally evaluated, which was the focus of the present study.…”

Section: Introductionmentioning

confidence: 99%

“…The purpose of the current study was to seek broad verification and validation of lumbar spine FE models created using a previously published automated algorithm (Campbell and Petrella, 2015). We defined our context of use as: prediction of torquerotation response, disc pressure, and facet force in flexion/extension, axial rotation, and lateral bending, under typical in vivo simulated loads.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Automated finite element meshing of the lumbar spine: Verification and validation with 18 specimen-specific models

Campbell

Coombs

Rao³

et al. 2016

Journal of Biomechanics

View full text Add to dashboard Cite

The purpose of this study was to seek broad verification and validation of human lumbar spine finite element models created using a previously published automated algorithm. The automated algorithm takes segmented CT scans of lumbar vertebrae, automatically identifies important landmarks and contact surfaces, and creates a finite element model. Mesh convergence was evaluated by examining changes in key output variables in response to mesh density. Semi-direct validation was performed by comparing experimental results for a single specimen to the automated finite element model results for that specimen with calibrated material properties from a prior study. Indirect validation was based on a comparison of results from automated finite element models of 18 individual specimens, all using one set of generalized material properties, to a range of data from the literature. A total of 216 simulations were run and compared to 186 experimental data ranges in all six primary bending modes up to 7.8Nm with follower loads up to 1000N. Mesh convergence results showed less than a 5% difference in key variables when the original mesh density was doubled. The semi-direct validation results showed that the automated method produced results comparable to manual finite element modeling methods. The indirect validation results showed a wide range of outcomes due to variations in the geometry alone. The studies showed that the automated models can be used to reliably evaluate lumbar spine biomechanics, specifically within our intended context of use: in pure bending modes, under relatively low non-injurious simulated in vivo loads, to predict torque rotation response, disc pressures, and facet forces.

show abstract

Section: Fe Model Detailsmentioning

confidence: 99%

Section: Fe Model Detailsmentioning

confidence: 99%

Section: Fe Model Detailsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Automated finite element meshing of the lumbar spine: Verification and validation with 18 specimen-specific models

Campbell

Coombs

Rao³

et al. 2016

Journal of Biomechanics

View full text Add to dashboard Cite

show abstract

Next-generation prognosis framework for pediatric spinal deformities using bio-informed deep learning networks

Tajdari

Shirzadian

et al. 2022

Engineering with Computers

View full text Add to dashboard Cite

Predicting pediatric spinal deformity (PSD) from X-ray images collected on the patient's initial visit is a challenging task. This work builds on our previous method and provides a novel bio-informed framework based on a mechanistic machine learning technique with dynamic patient-specific parameters to predict PSD. We provide a geometry-based bone growth model that can be utilized in a range of applications to enhance the bio-informed mechanistic machine learning framework. The proposed technique is utilized to examine and predict spine curvature in PSD cases such as adolescent idiopathic scoliosis. The best fit of a segmented 3D volumetric geometry of the human spine acquired from 2D X-ray images is employed. Using an active contour model based on gradient vector flow snakes, the anteroposterior and lateral views of the X-ray images are segmented to derive the 2D contours surrounding each vertebra. Using minimal user input, the snake parameters are calibrated and automatically computed over the dataset, resulting in fast image segmentation and data collection. The 2D segmented outlines of each vertebra are transformed into a 3D image segmentation result. The Iterative Closest Point mesh registration technique is then used to establish a mesh morphing approach and creates a 3D atlas spine model. Using the comprehensive 3D volumetric model, one can automatically extract spinal geometry data as inputs to the mechanistic machine learning network. Moreover, the proposed bio-informed deep learning network with the modified bone growth model achieves competitive or even superior performance against other state-of-the-art learning-based methods.

show abstract

Development of a multiscale model of the human lumbar spine for investigation of tissue loads in people with and without a transtibial amputation during sit-to-stand

Honegger

Actis

Gates

et al. 2020

Biomech Model Mechanobiol

View full text Add to dashboard Cite

An Automated Method for Landmark Identification and Finite-Element Modeling of the Lumbar Spine

Abstract: The automated methods developed represent advancement in the state of the art of subject-specific lumbar spine modeling to a scale not possible with prior manual and semiautomated methods.

Cited by 25 publications

References 38 publications

Automated finite element meshing of the lumbar spine: Verification and validation with 18 specimen-specific models

Automated finite element meshing of the lumbar spine: Verification and validation with 18 specimen-specific models

Next-generation prognosis framework for pediatric spinal deformities using bio-informed deep learning networks

Development of a multiscale model of the human lumbar spine for investigation of tissue loads in people with and without a transtibial amputation during sit-to-stand

Contact Info

Product

Resources

About